Intelligent hybrid automatic repeat request retransmission for multi‐band Wi‐Fi networks

Wang, Zhou; Fang, Xuming; Li, Yan; He, Rong; Long, Yan

doi:10.1049/cmu2.12158

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…As per the IEEE 802.11 standardization, we consider that the IoT device could transmit the same packet a total of seven times, which includes the initial transmission attempt. Hence, the maximum number of retransmission attempts (m) is six [24,25], the minimum contention window size CW min is 31 time slots, the maximum contention window size CW max is 1023 time slots, and the maximum number of recursive attempts to increase CW is 6 [24,25]. We define the collision probability for each transmission attempt as P c (n) and the transmission attempt probability as P a (n) in follows.…”

Section: Collision Probabilitymentioning

confidence: 99%

Energy-Efficient AP Selection Using Intelligent Access Point System to Increase the Lifespan of IoT Devices

Lee

Park

Choi

et al. 2023

Sensors

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With the emergence of various Internet of Things (IoT) technologies, energy-saving schemes for IoT devices have been rapidly developed. To enhance the energy efficiency of IoT devices in crowded environments with multiple overlapping cells, the selection of access points (APs) for IoT devices should consider energy conservation by reducing unnecessary packet transmission activities caused by collisions. Therefore, in this paper, we present a novel energy-efficient AP selection scheme using reinforcement learning to address the problem of unbalanced load that arises from biased AP connections. Our proposed method utilizes the Energy and Latency Reinforcement Learning (EL-RL) model for energy-efficient AP selection that takes into account the average energy consumption and the average latency of IoT devices. In the EL-RL model, we analyze the collision probability in Wi-Fi networks to reduce the number of retransmissions that induces more energy consumption and higher latency. According to the simulation, the proposed method achieves a maximum improvement of 53% in energy efficiency, 50% in uplink latency, and a 2.1-times longer expected lifespan of IoT devices compared to the conventional AP selection scheme.

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Section: Collision Probabilitymentioning

confidence: 99%

Energy-Efficient AP Selection Using Intelligent Access Point System to Increase the Lifespan of IoT Devices

Lee

Park

Choi

et al. 2023

Sensors

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“…Yano et al [167] solve this uncertainty by learning and predicting when a given interface will become idle using a probabilistic neural network (PNN). Multi-band operation is also considered by Wang et al [170] in combination with HARQ to improve packet retransmissions' efficiency. SL decides if a packet retransmission should use the same band as the previous transmission, a different one, or all available bands simultaneously by sending multiple copies.…”

Section: E Multi-link Operation Network Mimo and Full-duplexmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

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Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.

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“…With the increase of Wi-Fi connections and throughput, the hybrid automatic repeat request (HARQ) and multi-band technologies will be introduced to solve the problems of unstable delay and the poor throughput in dense Wi-Fi scenario in the next generation of Wi-Fi standard IEEE 802.11be in literature [22]. Literature [23] proposed the P2P intra-traffic file sharing architecture using a software defined network (SDN) system in TWDM PON to reduce internal PON and ISP traffic while improving the quality of service.…”

Section: Related Workmentioning

confidence: 99%

Cloud‐edge collaboration based peer to peer services redirection strategy for passive optical network

et al. 2022

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With the increasing number of fibre access users, P2P file sharing download traffic has gradually become one of the enormous bandwidth consumptions for the access network. Localizing traffic could save bandwidth and reduce latency, but it caused optical line terminal (OLT) overload problems. A cloud‐edge collaboration technology (IRS‐CECT) based P2P redirection strategy is proposed for this problem. OLT assumes the role of the cloud computing centre in the strategy. Optical network unit (ONU) serves as an edge computing node to share traffic load for the cloud computing centre. ONU intercepts the upstream query packets for OLT. This strategy can reduce the problem of OLT overload caused by increasing users. Simulation results indicate that the IRS‐CECT strategy can reduce the delay by nearly half and reduce the traffic load on the cloud computing centre OLT.

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Intelligent hybrid automatic repeat request retransmission for multi‐band Wi‐Fi networks

Cited by 4 publications

References 15 publications

Energy-Efficient AP Selection Using Intelligent Access Point System to Increase the Lifespan of IoT Devices

Energy-Efficient AP Selection Using Intelligent Access Point System to Increase the Lifespan of IoT Devices

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Cloud‐edge collaboration based peer to peer services redirection strategy for passive optical network

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